library(ggplot2)
library(readr)
library(knitr)
library(dplyr)
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library(tidyquant)
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library(tidyverse)
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library(sf)
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library(stringr)
permit_data <- tempfile()
download("https://raw.githubusercontent.com/WJC-Data-Science/DTS350/master/permits.csv", 
         permit_data, mode = "wb")
permits <- read_csv(permit_data)
## New names:
## Rows: 327422 Columns: 8
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): StateAbbr, countyname, variable dbl (5): ...1, state, county, year, value
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
head(permits)
## # A tibble: 6 × 8
##    ...1 state StateAbbr county countyname     variable     year value
##   <dbl> <dbl> <chr>      <dbl> <chr>          <chr>       <dbl> <dbl>
## 1     1     1 AL             1 Autauga County All Permits  2010   191
## 2     2     1 AL             1 Autauga County All Permits  2009   110
## 3     3     1 AL             1 Autauga County All Permits  2008   173
## 4     4     1 AL             1 Autauga County All Permits  2007   260
## 5     5     1 AL             1 Autauga County All Permits  2006   347
## 6     6     1 AL             1 Autauga County All Permits  2005   313
counties <- us_counties()
FIPSsetup<-permits%>%
  mutate(state=as.character(state), stateFIPS=str_pad(state, 2,pad="0")) %>%
  mutate(county=as.character(county), countyFIPS=str_pad(county, 3,pad="0"))
FIPSsetup
## # A tibble: 327,422 × 10
##     ...1 state StateAbbr county countyname     variable     year value stateFIPS
##    <dbl> <chr> <chr>     <chr>  <chr>          <chr>       <dbl> <dbl> <chr>    
##  1     1 1     AL        1      Autauga County All Permits  2010   191 01       
##  2     2 1     AL        1      Autauga County All Permits  2009   110 01       
##  3     3 1     AL        1      Autauga County All Permits  2008   173 01       
##  4     4 1     AL        1      Autauga County All Permits  2007   260 01       
##  5     5 1     AL        1      Autauga County All Permits  2006   347 01       
##  6     6 1     AL        1      Autauga County All Permits  2005   313 01       
##  7     7 1     AL        1      Autauga County All Permits  2004   367 01       
##  8     8 1     AL        1      Autauga County All Permits  2003   283 01       
##  9     9 1     AL        1      Autauga County All Permits  2002   276 01       
## 10    10 1     AL        1      Autauga County All Permits  2001   400 01       
## # … with 327,412 more rows, and 1 more variable: countyFIPS <chr>
FIPS <- FIPSsetup %>%
  mutate(geoid=paste0(stateFIPS,countyFIPS))
county_FIPS_data <- merge(FIPS,counties, by = "geoid") %>%
  group_by(state_name,year) %>%
  summarise(across(value, sum))
## `summarise()` has grouped output by 'state_name'. You can override using the
## `.groups` argument.
USA <- ggplot(data = county_FIPS_data, aes(x = year, y = value, color = state_name)) +
  geom_line() +
  geom_point() +
  labs(x = "Year", y = "Permit Amount") +
  scale_y_continuous(labels = comma) +
  theme_bw() +
  theme(legend.position = 'none') 
ggplotly(USA)
Missouri <- county_FIPS_data %>%
  filter(state_name == 'Missouri') %>%
  ggplot(aes(x = year, y = value)) +
  geom_point() +
  geom_line() +
  labs(title="Missouri",x = 'Year', y = 'Permit Amount') +
  scale_y_continuous(labels = comma) +
  theme_bw() +
  theme(legend.position = 'none')
Missouri

Kansas <- county_FIPS_data %>%
  filter(state_name == 'Kansas') %>%
  ggplot(aes(x = year, y = value)) +
  geom_point() +
  geom_line() +
  labs(title="Kansas",x = 'Year', y = 'Permit Amount') +
  scale_y_continuous(labels = comma) +
  theme_bw() +
  theme(legend.position = 'none')
Kansas

Nebraska <- county_FIPS_data %>%
  filter(state_name == 'Nebraska') %>%
  ggplot(aes(x = year, y = value)) +
  geom_point() +
  geom_line() +
  labs(title="Nebraska",x = 'Year', y = 'Permit Amount') +
  scale_y_continuous(labels = comma) +
  theme_bw() +
  theme(legend.position = 'none')
Nebraska

Iowa <- county_FIPS_data %>%
  filter(state_name == 'Iowa') %>%
  ggplot(aes(x = year, y = value)) +
  geom_point() +
  geom_line() +
  labs(title="Iowa",x = 'Year', y = 'Permit Amount') +
  scale_y_continuous(labels = comma) 
  theme_bw() +
  theme(legend.position = 'none')
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Iowa

grid.arrange(USA, Missouri, Kansas, Nebraska, Iowa, nrow = 3)

After finally being able to integrate the FIPS numbers with the permit data and after testing the results with surrounding border states of Missouri, that the midwest border states actually buck the trend shown in the United States wide chart. In the decade between 1990 and 2000, the majority of the U.S experienced a decrease in permits, but all of the states shown in the data above show growth or neutraility in that time period, with no sembelence of a decrease at all.